Say goodbye to spreadsheets
in NLU
Turn raw text data into NLU-ready datasets.
Top Canadian Bank
UK Online Directory
Multinational
Pharmaceutical
Large Online Retailer
Time to production
in hours, not months
Discover, label, and build intent taxonomies from real conversations with machine-learning assisted workflows. Improve and fine-tune with model-driven feedback.
Turn any source of text into training data
Train your conversational AI application on real life conversations. Build intent models that will reflect reality. Use any text source to get started.
Go live,
confidently
Find out what the machine understands with real-time, model-driven feedback. Run ondemand tests, compare evaluations, revert back to any version, and most importantly — don't be shocked by real production data.
Integrate with any Conversational AI platform
Our multilingual & modular data pipeline allows teams to deploy across usecases, technologies, and industries
Centralize & Collaborate with every stakeholder
Manage all of your projects in our
one-stop shop data hub, invite users to collaborate, share reports and results with any stakeholder — even the less technical people on your team!
Case Study
Top Shoe Manufacturer
Problem
Previous chatbot intent taxonomy built for customer support using manual workflows took 105 hours to build and had the following results: 140 labels created, 88% F1, 74% coverage
Solution
Rebuilt entire intent taxonomy using historical chat transcripts in under 18 hours. Results from intent taxonomy built in 18 hours: 256 labels created: 95 % F1 score, 92 % coverage
10x
productivity
gain
91%
F1 in first
iteration
92%
coveage in first
iteration
